PyViz Application

hvplot

Fig. 10 Python tools for data visualization PyViz.

dede1.

import os
import numpy as np
import xarray as xr

import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
cartopy.config['data_dir'] = os.getenv('CARTOPY_DIR', cartopy.config.get('data_dir'))

import cmocean

import holoviews as hv
from holoviews import opts, dim

import geoviews as gv
from geoviews import tile_sources as gvts
import geoviews.feature as gf
from cartopy import crs as ccrs

import hvplot.xarray
import panel as pn

import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning) 

gv.extension('bokeh')

year = 2018

# GBR4
base_url = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-"

biofiles = [f"{base_url}{year}-{month:02}.nc" for month in range(1, 2)]
biofiles

['http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-2018-01.nc']

ds_bio = xr.open_mfdataset(biofiles)
ds_bio

<xarray.Dataset>
Dimensions:          (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
    zc               (k) float64 dask.array<chunksize=(17,), meta=np.ndarray>
  * time             (time) datetime64[ns] 2018-01-01T02:00:00 ... 2018-01-31...
  * latitude         (latitude) float64 -28.7 -28.67 -28.64 ... -7.066 -7.036
  * longitude        (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
Dimensions without coordinates: k
Data variables: (12/101)
    alk              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    BOD              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    Chl_a_sum        (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    CO32             (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    DIC              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    DIN              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    ...               ...
    SGH_N            (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGH_N_pr         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGHROOT_N        (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGROOT_N         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    TSSM             (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    Zenith2D         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/20)
    Conventions:                     CF-1.0
    NCO:                             netCDF Operators version 4.7.7 (Homepage...
    RunID:                           2
    _CoordSysBuilder:                ucar.nc2.dataset.conv.CF1Convention
    aims_ncaggregate_buildDate:      2020-08-21T23:07:30+10:00
    aims_ncaggregate_datasetId:      products__ncaggregate__ereefs__GBR4_H2p0...
    ...                              ...
    paramfile:                       /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_C...
    paramhead:                       eReefs 4 km grid. SOURCE Catchments with...
    technical_guide_link:            https://eatlas.org.au/pydio/public/aims-...
    technical_guide_publish_date:    2020-08-18
    title:                           eReefs AIMS-CSIRO GBR4 BioGeoChemical 3....
    DODS_EXTRA.Unlimited_Dimension:  time
# base_url2 = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-"
# hydrofiles = [f"{base_url2}{year}-{month:02}.nc" for month in range(1, 2)]
# hydrofiles

# ds_hydro = xr.open_mfdataset(hydrofiles)
# ds_hydro

base_map = gvts.EsriImagery
crs = ccrs.PlateCarree()

ds_bio.coords['k'] = ('zc',ds_bio.zc)
ds_bio = ds_bio.swap_dims({'zc':'k'})
ds_bio = ds_bio.drop(['zc'])
ds_bio

<xarray.Dataset>
Dimensions:          (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
  * time             (time) datetime64[ns] 2018-01-01T02:00:00 ... 2018-01-31...
  * latitude         (latitude) float64 -28.7 -28.67 -28.64 ... -7.066 -7.036
  * longitude        (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
  * k                (k) float64 -145.0 -120.0 -103.0 -88.0 ... -3.0 -1.5 -0.5
Data variables: (12/101)
    alk              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    BOD              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    Chl_a_sum        (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    CO32             (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    DIC              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    DIN              (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
    ...               ...
    SGH_N            (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGH_N_pr         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGHROOT_N        (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    SGROOT_N         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    TSSM             (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
    Zenith2D         (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/20)
    Conventions:                     CF-1.0
    NCO:                             netCDF Operators version 4.7.7 (Homepage...
    RunID:                           2
    _CoordSysBuilder:                ucar.nc2.dataset.conv.CF1Convention
    aims_ncaggregate_buildDate:      2020-08-21T23:07:30+10:00
    aims_ncaggregate_datasetId:      products__ncaggregate__ereefs__GBR4_H2p0...
    ...                              ...
    paramfile:                       /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_C...
    paramhead:                       eReefs 4 km grid. SOURCE Catchments with...
    technical_guide_link:            https://eatlas.org.au/pydio/public/aims-...
    technical_guide_publish_date:    2020-08-18
    title:                           eReefs AIMS-CSIRO GBR4 BioGeoChemical 3....
    DODS_EXTRA.Unlimited_Dimension:  time
var = 'temp'
label = f'{ds_bio[var].long_name}: {ds_bio[var].units}'
mesh = ds_bio[var][:,:].hvplot.quadmesh(x='longitude',y='latitude',crs=crs, cmap='jet',
                           rasterize=True, groupby=list(ds_bio[var].dims[:2]), title=label, width=600,height=600)
overlay = (base_map * mesh).opts(active_tools=['wheel_zoom', 'pan'])
widgets = {dim: pn.widgets.Select for dim in ds_bio[var].dims[:2]}
dashboard = pn.pane.HoloViews(overlay, widgets=widgets).layout

dashboard

rho_vars = []
for var in ds_bio.data_vars:
    if len(ds_bio[var].dims) > 0:
        rho_vars.append(var)

var_select = pn.widgets.Select(name='Select variables:', options=rho_vars, 
                               value='temp')

crs = ccrs.PlateCarree()

base_map_select = pn.widgets.Select(name='Choose underlying map:', options=gvts.tile_sources, value=gvts.EsriImagery)

color_select = pn.widgets.Select(name='Pick a colormap', options= sorted([
    'cet_bgy', 'cet_bkr', 'cet_bgyw', 'cet_bky', 'cet_kbc', 'cet_coolwarm', 
    'cet_blues', 'cet_gwv', 'cet_bmw', 'cet_bjy', 'cet_bmy', 'cet_bwy', 'cet_kgy', 
    'cet_cwr', 'cet_gray', 'cet_dimgray', 'cet_fire', 'kb', 'cet_kg', 'cet_kr',
    'cet_colorwheel', 'cet_isolium', 'cet_rainbow', 'cet_bgy_r', 'cet_bkr_r', 
    'cet_bgyw_r', 'cet_bky_r', 'cet_kbc_r', 'cet_coolwarm_r', 'cet_blues_r', 
    'cet_gwv_r', 'cet_bmw_r', 'cet_bjy_r', 'cet_bmy_r', 'cet_bwy_r', 'cet_kgy_r', 
    'cet_cwr_r', 'cet_gray_r', 'cet_dimgray_r', 'cet_fire_r', 'kb_r', 'cet_kg_r', 
    'cet_kr_r', 'cet_colorwheel_r', 'cet_isolium_r', 'cet_rainbow_r', 'jet'], 
    key=str.casefold), value='jet') 

def plot(var=None, base_map=None, cmap='jet'):
    
    base_map = base_map or base_map_select.value
    var = var or var_select.value
    label = f'{ds_bio[var].long_name}: {ds_bio[var].units}'
    
    mesh = ds_bio[var].hvplot.quadmesh(x='longitude', y='latitude', rasterize=True, title=label,
                                    width=600, height=600, crs=crs,
                                    groupby=list(ds_bio[var].dims[:-2]), 
                                    cmap=cmap)
    
    mesh = mesh.redim.default(**{d: ds_bio[d].values.max() for d in ds_bio[var].dims[:-2]})
    overlay = (base_map * mesh.opts(alpha=0.9)).opts(active_tools=['wheel_zoom', 'pan'])
    widgets = {dim: pn.widgets.Select for dim in ds_bio[var].dims[:-2]}
    
    return pn.pane.HoloViews(overlay).layout #, widgets=widgets).layout

def on_var_select(event):
    var = event.obj.value
    dashboard[-1] = plot(var=var)
    
def on_base_map_select(event):
    base_map = event.obj.value
    dashboard[-1] = plot(base_map=base_map)
    
def on_color_select(event):
    cmap = event.obj.value
    dashboard[-1] = plot(cmap=cmap)

var_select.param.watch(on_var_select, parameter_names=['value']);
base_map_select.param.watch(on_base_map_select, parameter_names=['value']);
color_select.param.watch(on_color_select, parameter_names=['value']);

# dashboard = pn.Column(var_select, base_map_select, plot(var_select.value))
widget = pn.widgets.StaticText(name='', value='High-level dashboarding solution for interactive visualisation', 
                               style={'font-size': "14px", 'font-style': "bold"})

selection_widget = pn.Row(var_select, color_select, base_map_select)

dashboard = pn.Column(widget, selection_widget, plot(var_select.value))
box = pn.WidgetBox('# eReefs App', dashboard)

box.servable()

As you can see, the resulting object is rendered in the notebook (above), and it’s usable as long as you have Python running on this code. You can also launch this app as a standalone server outside of the notebook, because we’ve marked the relevant object .servable(). That declaration means that if someone later runs this notebook as a server process (using panel serve --show ereefs_app.ipynb), your browser will open a separate window with the serveable object ready to explore or share, just like the screenshot at the top of this notebook.

#! panel serve --show --port 5009 ereefs_app.ipynb

This web page was generated from a Jupyter notebook and not all interactivity will work on this website.

1

Signell & Pothina: Analysis and Visualization of Coastal Ocean Model Data in the Cloud, 2019.

eReefs Xarray maps Interactive Maps